neuronika
Tensors and dynamic neural networks in pure Rust. (by neuronika)
autograph
A machine learning library for Rust. (by charles-r-earp)
neuronika | autograph | |
---|---|---|
19 | 5 | |
1,073 | 325 | |
1.6% | 0.3% | |
0.0 | 9.0 | |
over 2 years ago | 8 months ago | |
Rust | Rust | |
Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
neuronika
Posts with mentions or reviews of neuronika.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-01-02.
- This year I tried solving AoC using Rust, here are my impressions coming from Python!
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Deep Learning in Rust: Burn 0.4.0 released and plans for 2023
Also perhaps comparing to Neuronika.
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Making a better Tensorflow thanks to strong typing
how does it compare with https://github.com/spearow/juice, https://github.com/neuronika/neuronika and https://github.com/spearow/juice?
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[D] To what extent can Rust be used for Machine Learning?
Check where and how this struct is used. https://github.com/neuronika/neuronika/blob/variable-rework/neuronika-variable/src/history.rs
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What do I need for an ML/DL based scripting language in Rust?
Also you can take a look at neuronika.
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ML in Rust
There is also https://github.com/neuronika/neuronika
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Enzyme: Towards state-of-the-art AutoDiff in Rust
I have a question: as the maintainer of [neuronika](https://github.com/neuronika/neuronika), a crate that offers dynamic neural network and auto-differentiation with dynamic graphs, I'm looking at a future possible feature for such framework consisting in the possibility of compiling models, getting thus rid of the "dynamic" part, which is not always needed. This would speed the inference and training times quite a bit.
- Any role that Rust could have in the Data world (Big Data, Data Science, Machine learning, etc.)?
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What sort of mature, open-source libraries do you feel Rust should have but currently lacks?
If you like autograd you will love neuronika
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bhtsne 0.5.0, now 5.6x faster on a 4 core machine, plus a summary of my Rust journey (so far)
After reading most of the book, I wanted to get my hands dirty. My initial idea was to build a small machine learning framework but I deemed it to be too difficult if not impossible for me at the time. (Now, neuronika would have something to say). When gathering the bibliography for my thesis, I recalled to have stumbled upon a particular algorithm, t-SNE, whom I liked very much. I found the idea behind it to be very clever and elegant (t-SNE it's still one of my favorite algorithms, together with backprop and SOM, I find manifold learning fascinating in general). "So be it", I said, and I began writing a mess of a code, that was basically a translation of the C++ implementation. Boy was it bad.
autograph
Posts with mentions or reviews of autograph.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-01-04.
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Where to Learn Vulkan for parallel computation (with references to porting from CUDA)
I'm working on a machine learning library https://github.com/charles-r-earp/autograph implemented in Rust that uses rust-gpu to compile Rust compute shaders to spirv, and then gfx_hal to target metal and dx12. Training performance is currently about 2x slower than pytorch (cuda) on my laptop but I've made significant progress recently and I am targeting 1.5x. While rust-gpu itself has it's own restrictions, it does support inline spirv assembly, which provides direct access to operations not provided in its std lib, thus it's lower level than GLSL. For example, it should be possible to target cuda tensor cores via cooperative matrix operations (I believe Metal supports these as well but this may not be implemented in spirv-cross and certainly isn't in naga). Once I have things a bit more stabilized I'd like to provide more examples, like porting from cuda / opencl, but I'm still figuring out patterns like how to work with 16 and 8 bit types in a nice and portable way.
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autograph v0.1.0
autograph v0.1.0
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What's the current state of GPU compute in rust?
Working on autograph, for machine learning and neural networks. Unlike CUDA / HIP it's threadsafe, but doesn't expose low level things like multiple streams. Most of the shaders are glsl but I'm now using rust_gpu for pure rust gpu code.
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Announcing neuronika 0.1.0, a deep learning framework in Rust
Maybe not for learning but as inspiration I have to plug this amazing effort for ML with (vulkan) shaders: https://github.com/charles-r-earp/autograph
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What do you think about a library that helps reducing the overhead of GPU programming, regarding ndimensional Arrays?
Maybe you'd be interested in checking out my library, https://github.com/charles-r-earp/autograph?
What are some alternatives?
When comparing neuronika and autograph you can also consider the following projects:
rust-ndarray - ndarray: an N-dimensional array with array views, multidimensional slicing, and efficient operations
petgraph - Graph data structure library for Rust.
tractjs - Run ONNX and TensorFlow inference in the browser.
ocl - OpenCL for Rust
skytable - Skytable is a modern scalable NoSQL database with BlueQL, designed for performance, scalability and flexibility. Skytable gives you spaces, models, data types, complex collections and more to build powerful experiences
juice - The Hacker's Machine Learning Engine